Ultrasonic scanners for detecting blood flow based on the Doppler effect are well known. Such systems operate by actuating an ultrasonic transducer array to transmit ultrasonic waves into the object and receiving ultrasonic echoes backscattered from the object. In the measurement of blood flow characteristics, returning ultrasonic waves are compared to a frequency reference to determine the frequency shift imparted to the returning waves by flowing scatterers such as blood cells. This frequency shift translates into the velocity of the blood flow.
In state-of-the-art ultrasonic scanners, the pulsed or continuous wave (CW) Doppler waveform is computed and displayed in real-time as a grey-scale spectrogram of velocity versus time with the grey-scale intensity (or color) modulated by the spectral power. The data for each spectral line comprises a multiplicity of frequency data bins for different frequency intervals, the spectral power data in each bin for a respective spectral line being displayed in a respective pixel of a respective column of pixels on the display monitor. Each spectral line represents an instantaneous measurement of blood flow.
FIG. 1 is a block diagram of the basic signal processing chain in a conventional spectral Doppler mode. An ultrasound transducer array 2 is activated to transmit by a transmit ultrasound burst which is fired repeatedly at a pulse repetition frequency (PRF). The PRF is typically in the kilohertz range. The return RF signals are detected by the transducer elements and then formed into a receive beam by a beamformer 4. For a digital system, the summed RF signal from each firing is demodulated by demodulator 6 into its in-phase and quadrature (I/Q) components. The I/Q components are integrated (summed) over a specific time interval and then sampled by block 8. The summing interval and transmit burst length together define the length of the sample volume as specified by the user. The "sum and dump" operation effectively yields the Doppler signal backscattered from the sample volume. The Doppler signal is passed through a wall filter 10 which rejects any clutter in the signal corresponding to stationary or very slow moving tissue. The filtered output is then fed into a spectrum analyzer 12, which typically takes Fast Fourier Transforms (FFTs) over a moving time window of 64 to 128 samples. Each FFT power spectrum is compressed (block 14) and mapped (block 16) to a grey scale for display on monitor 18 as a single spectral line at a particular time point in the Doppler velocity (frequency) versus time spectrogram.
The automatic Doppler waveform tracing (block 20) is performed after the FFT power spectrum x is compressed in accordance with a compression function h(x)=y and converted to grey map values in accordance with a mapping g(y)=z. The computed maximum/mean velocity traces are usually presented as overlay information on the spectrogram display. Whereas the mean frequency or velocity is defined by the first moment of the Doppler spectrum, the maximum frequency can be challenging to detect in a consistent manner, especially under weak SNR conditions.
One of the primary advantages of Doppler ultrasound is that it can provide noninvasive and quantitative measurements of blood flow in vessels. Given the angle .theta. between the insonifying beam and the flow axis, which is usually specified by rotating a cursor line in the B-mode image of a duplex scan, the magnitude of the velocity vector can be determined by the standard Doppler equation: EQU v=cf.sub.d /(2f.sub.0 cos .theta.)
where c is the speed of sound in blood, .function..sub.0 is the transmit frequency and .function..sub.d is the motion-induced Doppler frequency shift in the backscattered ultrasound. In practice an intensity-modulated Doppler frequency versus time spectogram is displayed since the Doppler sample volume or range cell generally contains a distribution of velocities that can vary with time. Of special importance is the maximum frequency (.function..sub.max) waveform or "envelope" of the Doppler spectrogram, because its value at different points in the cardiac cycle is used in a number of diagnostic indices. In fact, it has been reported that an abnormally high .function..sub.max or v.sub.max at peak systole alone is a good indicator of vascular stenosis. Also, v.sub.max is used to estimate the pressure drop across a stenosis based on the Bernoulli equation.
In the conventional ultrasound scanner shown in FIG. 1, the Doppler spectrogram is computed via the FFT. Various methods have been developed to automatically trace the .function..sub.max waveform over a white noise background. A summary of these methods for detection of .function..sub.max in the FFT spectrogram can be found in an article by Mo et al. entitled "Comparison of Four Digital Maxi-mum Frequency Estimators for Doppler Ultrasound," Ultrasound Med. Biol., Vol. 14, pp. 355-363 (1988). For implementation on a real-time Doppler system, similar techniques have been proposed (see, e.g., U.S. Pat. No. 5,287,753) for compressed Doppler spectrograms with 6-8 bits of display dynamic range. However, it has been reported that the maximum velocity estimates can be 10-60% higher than the actual maximum velocity within the sample volume (see Daigle et al., "Overestimation of Velocity and Frequency Values by Multi-Element Linear Array Probes," J. Vasc. Technology, Vol. 14, pp. 206-213 (1990) and Hoskins et al., "Velocity Estimation Using Duplex Scanners," Ultra-sound Med. Biol., Letter to the Editor, Vol. 17, pp. 195-198 (1991)). Besides statistical and operator's variability, a significant portion of this error can be attributed to intrinsic Doppler spectral broadening. This problem is compounded by the fact that the amount of intrinsic spectral broadening is generally dependent on beamforming as well as signal processing parameters.
The published literature on Doppler spectral broadening can be divided into two distinct camps. One camp considered only the spectral broadening associated with a finite bandwidth pulse, which is a manifestation of the fundamental tradeoff between spatial (time) and velocity (frequency) resolution. The other camp assumed narrowband excitation (CW Doppler or pulsed Doppler with a long range cell) and focused on diffraction broadening. A geometric explanation of diffraction broadening is that the rays emanating from the different transducer array elements form different angles with the scatterer velocity vector. Diffraction broadening is also sometimes described as a finite transit time effect for scatterers moving across the sound beam.
Most, if not all, of the spectral broadening reduction or correction methods which have been proposed actually deal only with the diffraction component. The simplest method is to reduce the transducer active aperture at the expense of decreased spatial resolution and sensitivity, as disclosed in the Daigle et al. and Hoskins et al. references cited above. The theory of Newhouse et al., set forth in "The Dependence of Ultrasound Doppler Bandwidth on Beam Geometry," IEEE Trans. Sonics and Ultrasonics, Vol. SU-27, pp. 50-59 (1980), has also been applied to correct the .function..sub.max estimates for diffraction broadening. See, e.g., Winkler et al., "Correction of Intrinsic Spectral Broadening Errors in Doppler Peak Velocity Measurements Made with Phased Sector and Linear Array Transducers," Ultrasound Med. Biol., Vol. 21, pp. 1029-1035 (1995); Newhouse et al., "Invariance of Doppler Bandwidth with Flow Axis Displacement," Proc. IEEE Ultrasonics Symp., pp. 1533-1536 (1990); Newhouse et al., "Study of Vector Flow Estimation with Transverse Doppler," Proc. IEEE Ultrasonics Symp., pp. 1259-1263 (1991); Tortoli et al., "Invariance of the Doppler Bandwidth with Range Cell Size Above a Critical Beam-to-Flow Angle," IEEE Trans. Ultrason., Ferroelec. and Freq. Control, Vol. UFFC-40, pp. 381-386 (1993). In U.S. Pat. No. 5,606,972, three additional methods have been proposed: (1) perform frequency-to-velocity conversion based on the smallest Doppler angle formed between the outermost element of the active aperture and the flow axis; (2) deconvolve the Doppler spectrum by an array distortion function; and (3) use Doppler reference signals for each element of the array which are a function of the position of the individual elements in the array aperture. The first method had been described earlier by Hoskins et al. The second method utilizes the theory of Newhouse et al. and assumes the actual velocity spread is very small--which may not be true for larger sample volumes. The third method requires a beamformer which is significantly more complex than those used for conventional methods.
Besides bandwidth and diffraction broadening, there is a third source of intrinsic spectral broadening referred to as analysis time broadening, which represents the frequency resolution corresponding to a finite FFT data window. The width of this data window is usually chosen to be around 10 msec, which is a compromise between frequency resolution and flow stationarity considerations. For a 10-msec window, analysis time broadening is usually small relative to bandwidth and diffraction broadening.
The study by Wilson ("Description of Broad-Band Pulsed Doppler Ultrasound Processing Using the Two-Dimensional Fourier Transform," Ultrasonic Imag., Vol. 13, pp. 301-315 (1991)) is perhaps the only one that has addressed all three types of intrinsic spectral broadening. This work, which is based on two-dimensional FFT analysis, gives an explicit expression for each type of broadening. However, it does not show whether or how the three types might interact with one another. It was only speculated that the total effect may be given by the square root of the sum of squares of the three types of broadening, as though they represent the standard deviation of independent random variables.